Trunk stability

How to use the concept

Start by writing down the event that begins the clock, the event that ends it, and the population that belongs in the denominator. For trunk stability, this boundary matters because retries, approvals, parallel work, and excluded services can otherwise change the result without changing the underlying workflow.

A concrete delivery example

Suppose a service records 100 relevant events during a month and 8 meet the condition represented by trunk stability. Under that explicit rule, the reported share is 8 percent. The team then reviews the eight cases individually, because the aggregate says how often the condition appeared but not why it appeared.

Limitations and interpretation

This signal is sensitive to instrumentation. Missing deployments, duplicate webhooks, hidden retries, and inconsistent labels can make trunk stability look better or worse. Preserve event-level evidence and record the policy used to include or exclude cases.

Before acting on trunk stability, compare the current observation with a compatible baseline and ask which underlying event produced it. Keep the raw records, counting policy, and ownership visible. When the signal changes, inspect the surrounding workflow for queueing, rework, failed checks, or recovery work. That practice makes the glossary term useful for diagnosis rather than a label attached to a dashboard.